Causal inference with multiple treatments Matched methods using propensity scores are a recommended form of analysis to identify causes and effects from non-clinical data. However, current matched methods are mostly limited to comparing one exposure type versus another. Prominent in this limitation is a difficulty in performing causal inference in the multi-treatment setting. However, causal methods for multiple treatments are often required in an aging population where the number of available therapies or interventions for common ailments such as arthritis or heart disease is growing. We propose to develop a method for a matched comparison of three exposure levels, which allows us to estimate the causal effects of three anti-rheumatic therapies on rheumatoid arthritis outcomes and of three types of caregivers on outcomes in a nursing home data set.